Urban Air Mobility: An IoT Perspective

Urban Air Mobility: An IoT Perspective

Item Type Article

Authors Biswas, Karnika; Ghazzai, Hakim; Sboui, Lokman; Massoud, Yehia Mahmoud

Citation Biswas, K., Ghazzai, H., Sboui, L., & Massoud, Y. (2023). Urban Air Mobility: An IoT Perspective. IEEE Internet of Things Magazine, 6(2), 122–128. https://doi.org/10.1109/iotm.001.2200237

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DOI 10.1109/iotm.001.2200237

Publisher Institute of Electrical and Electronics Engineers (IEEE) Journal IEEE Internet of Things Magazine

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Urban Air Mobility: An IoT Perspective

Karnika Biswas, Hakim Ghazzai, Senior Member, IEEE, Lokman Sboui, Member, IEEE, and Yehia Massoud, Fellow, IEEE

Abstract—Urban air mobility (UAM) is predicted to be an necessary part of transportation in the coming years. UAM is also expected to offer faster and environmentally friendly travel than ground-based transportation. UAM is envisioned as a piece of the smart living infrastructure, which should benefit from data-driven technology. This article discusses the various aspects of establishing a reliable and robust information exchange for an Internet-of-things (IoT) supported UAM network. The IoT framework can address the control, safety, security, and communication challenges of UAM by facilitating real-time data collection, efficient data routing, monitoring safety-critical system performance, improving operations management, and preserving data security. Consequently, UAM users can benefit from reliable, uninterrupted, on-demand services with privacy protection and real-time notifications. This article explains how restructured communication paradigms can assist in an effective integration of UAM and IoT into a collaborative ecosystem.

Index Terms—Internet of Things, urban air mobility, commu- nication, integration of services.

I. INTRODUCTION

During the last few decades, urban population density has witnessed a steep rise leading to an unprecedented increase in traffic congestion and commuting delays. Urban Air Mobility (UAM) has emerged to alleviate the saturation of growing ground traffic by offering alternative inter-city and intra-city air transport for passengers and merchandise using electric air taxis and delivery drones. UAM is intended to provide low altitude and short-range express air transportation with a zero- emission footprint and optimized utilization of resources [1].

As part of a smart city initiative, local authorities expect UAM to play a crucial role in the rapid deployment of a variety of services, such as medical assistance, rescue missions, and the delivery of shipments.

Despite being a very novel concept, the evolution of UAM is transpiring rapidly to be an integral part of smart mobility infrastructure. The race to develop UAM is ongoing between many multinational companies; for instance, Audi has recently demonstrated the successful execution of landing, takeoff, and cruise control of a scaled model of a passenger air taxi in urban areas. However, the acceptance and seamless assimilation of UAM into an intelligent living environment would require the electric aircraft to be safe, noiseless, and cost-competitive carrier [2]. Therefore, the UAM ecosystem must be fully compliant with the regulations proposed by the authorities

Karnika Biswas, Hakim Ghazzai, and Yehia Massoud are with the Computer, Electrical, Mathematical Science and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. (E–mails:

{karnika.biswas,hakim.ghazzai,yehia.massoud}@kaust.edu.sa).

Lokman Sboui is with the Systems Engineering Department, ´Ecole de Technologie Sup´erieure ( ´ETS), University of Qu´ebec, Montr´eal, Canada.

(Email: lokman.sboui@etsmtl.ca)

of civil aviation and certified by organizations such as the European Union Aviation Safety Agency (EASA) and the US Federal Aviation Administration (FAA).

UAM primarily uses Vertical TakeOff and Landing (VTOL) technology offering a sustainable solution to pollution, eco- nomic viability, enhanced safety, and improved fault toler- ance. Moreover, VTOL are expected to be mainly powered electrically (eVTOLs) and can operate without a runway, leading to extreme space and time savings. Nevertheless, many challenges remain to be addressed in order to make UAM available and operable on demand [3]. For instance, UAM encounters challenges associated with its operation and con- nectivity. Information exchange in UAM is subjected to com- munication interference and disturbances due to low-altitude flight characteristics. In addition, security concerns related to control and communication paradigms are of the utmost importance. Congestion management, air-traffic routing, and reconfiguration, particularly during takeoff and landing, are critical to the UAM ecosystem and are far more intricate and accident-prone than traditional high-altitude air travel. With the emergence of UAM comes the challenge of multi-agent coordination within the UAM fleet. The strategic scheduling and management of resources and tasks among eVTOLs flying in swarms make the process very challenging. Given the busy urban environment, UAM requires robust and scalable infras- tructure that enables the effective operation of the technology.

The low-altitude avionics come with challenges that are unique to UAM. These challenges can be effectively ad- dressed by leveraging the current upward trend of digital platforms, data manipulation, and data sharing. Integrating a list of sensors, services, networking protocols, end-users, and interface layers to execute the aforementioned set of tasks converts the UAM into a complex flying Internet of Things (IoT) system. In fact, IoT can provide a single, cohesive infrastructure offering a shared data pool and core support for managing the resources and services of UAM. Therefore, UAM-IoT integration can improve the safety and security via real-time alerts and warnings to the users and operators and enable the aerial vehicles to operate in a more eco-friendly and efficient manner. UAM-IoT will also provide a wide range of services, such as delivery, inspection, surveillance, entertainment, and emergency response, and can open up new business models and revenue streams. Recent reports have indicated that benefactors of UAM, like Audi and Airbus, are currently evaluating new IoT technologies and solutions with regards to battery management, urban flight regulation, certification, and infrastructure.

In this paper, we aim to analyze the potential of inte- grating UAM and IoT into a single framework. We the layers, connectivity options, and features of this integration.

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Fig. 1:Taxonomy demonstrating different aspects of Urban Air Mobility.

Furthermore, we highlight how IoT can help mitigate the UAM challenges and what are the possible IoT-enabled services that can be leveraged in UAM. Finally, we shed light on open research directions to adress challenges related to the UAM- IoT integration.

II. OVERVIEW OF THEUAM ECOSYSTEM

A. Current UAM Implementations

Several companies and air traffic regulatory authorities have joined efforts to frame regulations for airspace integration for UAM. The outcome led to a successful launch of the world’s first vertiport (a designated urban airport for vertical takeoff and landing) in Coventry, UK¹. The broader aim of such consortium is to increase awareness among the stake- holders and the consumers and to promote social acceptance of UAM. The design and development of eVTOL aircraft for commercial UAM started worldwide in 2018 under several banners, including Joby Aviation, Uber, Airbus, Kitty Hawk, Toyota, Aeromobil, Suzuki, etc. Presently, flight testing of the Google Wing project is set to span over 60 cities globally, with Volocopter promising to start operation in Europe by the end of 2022.

Fig. 1 presents a high-level taxonomy of the existing UAM ecosystem and its related technologies. eVTOLs can carry from one to nine passengers (or no passengers at all), depending on the model. These vehicles can be manually, remotely, or autonomously operated. For propulsion, they may use hydrogen, electricity, or a hybrid combination of the two (as adopted by Honda Motors for intercity air travel). eVTOLs may be further categorized according to the generation and allocation of thrust as follows:

• Vectored Thrust: A vector propulsion technology is being developed without separate thrust engines for hovering and cruising. This technology would combine multiple thrusters for lift and forward flight. A non-rotating vector propulsion

1https://skyports.net/2022/03/uk-consortium-completes-urban-air-mobility- concept-of-operations-for-the-civil-aviation-authority/

technology is being developed that is envisioned to simplify the avionics and provide more possibilities for air-frame designs.

•Lift and Cruise – In this mode, the aircraft utilizes separate and unique propulsion systems for vertical take-off and flight.

•Wingless Multicopter: This aircraft does not possess wings;

instead, multiple rotors are arranged symmetrically around its longitudinal axis and powered to provide thrust for hovering.

•Hover Bikes: This is a single-passenger eVTOL aircraft that enables the pilot to occupy a saddle on the aircraft and use cyclic control for maneuvering.

• Electric rotorcraft: This is a rotor-powered aircraft with cyclic/collective pitch control, such as an electric helicopter.

The vectored thrust and wingless multi-copter models are expected to be the leading eVTOL segments in the near future, due to their high efficiency and robust control. Examples of the different eVTOLs’ categories are illustrated in Fig. 2. In the near future, it is expected that UAM will cover urban airspace to provide a variety of services, such as passenger commuting,

Fig. 2:Different eVTOL categories from manufacturers like Lilium GmbH, Airbus Helicopters, Wisk Aero, Aerwin’s Technologies and Volocopter.

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Fig. 3: IoT architectural layers, applications, and systems for UAM integration.

package delivery, emergency medical airlifting, agricultural lands, forests, coastlines, and rescue missions. To enable these services, intelligent system integration is needed to address operational elements of UAM networks, such as navigation and control of eVTOLs, air-traffic control of UAM services, monitoring flight control statistics, battery management for aircraft, and effective resource and task management for UAM fleets, taking into account the dynamic nature of the environ- ment and current demand. Faster proliferation will result in a ubiquitous network of flying and connected eVTOLs.

B. UAM: A Complex Flying IoT System

UAM systems are expected to form complex IoT networks, supported by continuous real-time sensing, data analytics, and AI mechanisms, to facilitate the configuration, valida- tion, and maintenance of various operational challenges of eVTOLs. These challenges include navigation and guidance, air traffic management, flight control, emergency response, battery power management, and resource/task allocation. In- stallation of flight-data monitoring devices is also necessary for evaluating the battery level of onboard IoT subsystems, ensuring safety against system faults and malfunctions, and providing assistance in post-failure assessments. As an exam- ple, Rolls Royce is currently collaborating with Microsoft to use its Azure IoT Suite and Cortana Intelligence Suite for diagnosis of potential faults in the eVTOL engines. Besides, UAM must be well-equipped to obtain real-time and accurate updates about position, heading, speed, tilt, and altitude of its aircraft for efficient implementation of motion autonomy or guided navigation. IoT-connected smart sensors can also provide shorter maintenance delays, a better in-flight expe- rience, faster notifications, and accurate tracking facilities for multiple applications such as air taxis, logistic delivery, monitoring lands/objects, providing medical assistance, rescue, and military operations. A swarm of eVTOLs may be config- ured to operate as networked agents, where coordination and sharing of critical resources and assignments are important in maintaining task completion efficiency.

The well-established IoT framework can be leveraged to effectively address these features and challenges associated with UAM operations. Moreover, the seamless integration of the IoT into the UAM operation must be emphasized to ensure secure and uninterrupted data sharing among the flying units as well as secure data transactions among service providers, stakeholders, and end-users.

III. IOT FRAMEWORK FORUAM

As a complex flying IoT system, UAM is expected to connect resources, service providers, and end-users to provide smart air mobility. In this section, we first present the UAM- IoT framework in terms of functional layers and connectivity infrastructure. Afterwards, we discuss how IoT can play an essential role in solving many UAM challenges.

A. UAM-IoT Layers

The UAM-IoT functionalities are broadly divided into five layers, depicted in Fig. 3. Each layer operates in sequence and communicates with its adjacent layers through an encrypted data network. The functions performed by each layer are summarized as follows:

• Perception Layer: This is the bottom-most layer consist- ing of physical sensors, which are autonomous with UAM, enabled to generate raw data from the environment in real- time. A typical perception layer may include one or more sensors such as a thermocouple, resistance thermal detector, thermostat, smart pressure detector, altimeter, magnetometer, accelerometer, gyroscope, LiDAR, camera, and GNSS. The generated data is processed for undertaking intelligent and safe navigation and accurate control decisions.

• Network Layer: This layer is responsible for communi- cation between an eVTOL and the air traffic control (ATC) station, different underlying modules of an intra-eVTOL sys- tem, and also between multiple flying eVTOLs sharing the airspace. The network layer pertains to the connectivity of the sensors with data processing elements and deals with

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Fig. 4:UAM connectivity architecture.

data transmission using different communication technologies, routing of information using suitable networking protocols, data storage, and data security.

• Data Processing Layer: This layer imparts intelligence to the IoT framework and allows the data processing devices to extract useful information from the data pool. This data allows the UAM system to take decisions to complete the tasks successfully. Indexing of information, integration, analysis, and control are the fundamental objectives of this layer.

• Application Layer: The application layer is an end-user interface with data processing devices. The applications are related to the eVTOL configuration, operational support, and data access management support for end-users.

•Business Layer: This is the topmost decision-making layer that determines the business rules and process management schemes and profitable consumer packages by considering feedback from the end-users, depending on the UAM applica- tion.

B. UAM-IoT Data Generation

The eVTOLs are expected to generate a huge amount of IoT data from a variety of sources, including sensors, cameras, navigation systems, and communication systems. Joby Avia- tion indicated that their first pre-production prototype capable of carrying two passengers would generate around 65 terabytes of data in 2021. Some estimates suggest that an eVTOL could generate up to 20 terabytes of data per hour per flight.

However, such estimates are not accurate at the current stage since UAM technology is still subject to many improvements and upgrades. From an IoT perspective, eVTOLs are expected

to be similar to self-driving vehicles. Studies have shown that the amount of data generated by autonomous vehicles can vary depending on the number and type of sensors installed.

Statistics have shown that approximately 4 terabytes of data may be generated per day per vehicle, escalating the bandwidth requirement to 40 GBit/s. Among the types of data that may be generated by eVTOLs, we can list the following:

• Flight data: This includes data related to the position, velocity, and other flight parameters of the eVTOL, as well as data related to the performance and condition of the flying vehicle.

• Sensor data: This includes data from sensors such as cameras, LiDAR, radar, and other sensors that are used for navigation and control as well as obstacle detection.

• Communication data: This includes data related to the the position and movement of other eVTOLs and ground vehicles, as well as data on weather conditions and other factors that can affect the performance of the eVTOL.

•Passenger data: This includes data related to the passengers on board the eVTOL, if any, such as biometric data, and information about their journey and their personal usage.

C. UAM-IoT Connectivity

As with any IoT system, connectivity is a fundamental aspect that needs to be well defined for UAM systems. Design- ing communication systems for integrating the IoT framework with the UAM architecture is a key challenge. Connectivity is necessary to be established between:

•eVTOL-to-ATC station: The air traffic control (ATC) station is a central entity established to monitor and control all

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the air traffic within a specific UAM zone. The ATC is managed by professional and certified experts that require real- time and accurate information about the existing eVTOLs.

A continuous and ultra-reliable data exchange needs to be guaranteed between every eVTOL and the ATC station.

• Inter-eVTOL: Several eVTOL aircraft may be intercon- nected in the form of collaborating agents sharing a common communication network and performing coordinated and/or cooperative motion. Each of the aircraft in the swarm may be connected to the central network and may also exchange data with one or more eVTOLs. Using this configuration, the networked eVTOLs may be enabled to share resources and tasks for efficient utilization of the UAM services.

• Intra-eVTOL: Data exchange will be required within the same eVTOL aircraft to interconnect the different modules constituting an eVTOL. While the two previous data exchange modes necessitate wireless communication links, this mode can be enabled to support a wired intranet network within the aircraft.

EVTOLs can resort to different wireless connectivity modes to communicate with the external environment as illustrated in Fig. 4 [4]. These communication technologies differ in terms of availability, cost, and the quality of the channel.

Generally, flying cars communicate through a line-of-sight (LoS) link at sufficiently high altitudes. However, with ground units such as the ATC stations, they may suffer from non- LoS, which may degrade the communication. In all cases, the connectivity is subject to large-scale and small-scale losses due to obstacles and multiple path propagation of the signals. The communication links that an eVToL can adopt are summarized as follows:

• Ground cellular base station to eVTOL Link: UAM network coverage can be attained by a set of terrestrial cellular antennas pointing towards the sky. This type of communication link includes both LoS and NLoS components. The existence of already-deployed ground infrastructure, especially in urban areas, guarantees the permanent availability of such commu- nication links for eVToLs.

• Low and High Altitude Flying Platforms to eVTOL Link: Low-flying and tethered UAVs or balloons as well as high-altitude platforms can act as connectivity providers for eVTOLs. They principally provide LoS links that might be more impacted by the mobility of both flying transceivers and their respective velocities, i.e., the Doppler effect. A dedicated aerial infrastructure needs to be made available to maintain a permanent connectivity of the eVToLs. Otherwise, temporary data transmission can be performed depending on the availability of the flying transceivers.

•Satellite to eVTOL Link: With the expansion of Low Earth Orbit (LEO)-based satellite communications, eVTOLs can be equipped with a satellite-based transceiver communicating with one or more LEO constellations. The signal transmission loss between an eVTOL and a satellite comprises the free space path loss, atmospheric loss, polarization loss, and loss due to misalignment of the antenna. Communication can be designed given that the path loss is minimum for an orthogonal elevation angle and increases as the elevation angle is reduced.

• eVTOL to eVTOL Link: Information exchange between

Fig. 5:UAM paradigms as IoT framework.

participants of an eVTOL swarm can be implemented us- ing Machine-to-Machine (M2M) communication and Wireless Mesh Networks (WMN) [5]. An access point-free Flying Ad- hoc Network Architecture (FANET) where at least one of the eVTOLs is connected to the central network can be a suitable solution.

For the different connectivity links, power saving remains one of the most important factors that needs to be considered when designing communication technologies for eVTOLs as it can help save the battery of the vehicle and extend its range. This involves IoT-based power management techniques for limiting the amount of data transmission and turning off unused communication interfaces. Furthermore, suitable data compression and/or data sampling rates can be adopted to execute data-driven power management. Energy-efficient cooperative relaying schemes can be designed to reduce the global energy consumption of eVTOL networks based on state-of-the-art IoT devices that are capable of precisely measuring current and voltage consumption depending on the operating mode. In practice, Azure Stream Analytics and Microsoft Power BI dashboards are currently used by the aviation industry to improve their operational performance and increase energy efficiency.

D. UAM-IoT Paradigms and Functions

The aforementioned IoT layers supporting the UAM ecosys- tem as well as the diverse connectivity models and types are instrumental in improving multiple functionalities and processes of the UAM in terms of reliability, efficiency, and operation, as illustrated in Fig 5. In the following, we present the main functionalities that can be significantly leveraged by the IoT:

• Networking: As mentioned earlier, eVTOLs can act as flying IoT devices and form a FANET, enabling the vehicles to exchange information about their operations (e.g., travel plan and trajectory, environmental information, and real-time locations) with their peers to synchronize their navigation and prevent the risk of collision. Borrowing IoT routing protocols that are adaptive to dynamic network topologies, as in the UAM system, is essential to guarantee seamless and reliable connectivity [6]. As an example, a Franco-Dutch airline has conducted a pilot study to evaluate the effectiveness of an IoT service for tracking maintenance equipment, utilizing a distributed LoRa network of devices supplied by KPN, a Dutch telecommunications company. On the other hand, traditional hardware-specific networking algorithms are not sufficient to promote desired qualities such as flexibility, reconfigurability,

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and programmability of the eVTOL communication systems.

Therefore, additional functionalities based on IoT-networking protocols are necessary, for slicing the infrastructural resources of the network into several logical and virtual sub-networks to offer greater stability and autonomy in multiple UAM applica- tions such as eVTOL-passenger communication, inter-eVTOL communication, and eVTOL-central network communication.

A new IoT trend of probability-calculation based segmentation routing can be useful for addressing scalability, mobility and unbalanced load conditions in UAM ecosystem.

•Data Sensing and Processing: It is necessary to design the UAM architecture in a modular fashion, where each module is connected to other modules by encrypted links. The concept of decentralization prevents a malfunctioning module from affecting other modules. Each module might be autonomously operated to sense the external environment, predict the eVTOL status, and take the relevant actions. In this sense, machine learning and artificial intelligence algorithms coupled with the IoT framework will be the modern key elements for building data-driven modules in UAM to operate in real-time, detect anomalies, predict abnormal behaviors, generate alerts, and also monitor changes in trends and patterns. Therefore, fast data processing is essential for responding to real-time dynam- ics. Thanks to its dynamic and ubiquitous architecture, the IoT can provide different levels of computation capabilities at the edge, fog, and cloud layers to support the local computational resources of the eVTOL in enabling rapid data processing and executing machine/deep learning algorithms to better assess the situation. Piezo-film sensors, which are high-sensitivity vibration sensors, are becoming more and more popular for providing predictive maintenance, reducing downtime costs, and ensuring reliable and stable performance parameters like wide frequency response, high resolution, and low drift. Addi- tionally, embedded thermocouples and accelerometers, as well as multichannel hall-effect speed sensors, are being utilized as well in order to achieve the same goals.

• Data Computing: The most popular IoT-based computing and data storage infrastructure is cloud computing, which deals mainly with big data processing, business logic, and data warehousing. Information transfer in UAM-IoT integration can benefit from protocols like Message Queue Telemetry Transport (MQTT), Open Platform Communications Unified Architecture (OPC-UA), and various cloud-based messaging services. These protocols are processed with secure access control using Lightweight Directory Access Protocol (LDAP) and Identity and Access Management (IAM) systems. Addi- tionally, edge computing on resource-constrained IoT devices is experiencing rapid development due to the incorporation of AI accelerators and interfacing engines. These accelerators allow for improved performance and better data processing capabilities, while the interfacing engines enable the devel- opment of more efficient communication channels between devices. With edge computing, it is possible to process large volumes of real-time data with minimal data storage. UAM can benefit from cloud computing in complex computing environments and for delay-tolerant applications, whereas edge computing can be used for programs that require fast updates and use low bandwidth. For instance, cloud computing can

prove to be highly beneficial for the storage and processing of large amounts of data associated with flight planning, traffic management, predictive maintenance, and remote monitoring and control. On the other hand, edge and fog computing can be used for time-sensitive applications such as flight control, obstacle detection, and autopilot. In many cases, a hybrid computing solution that incorporates cloud and edge components may be necessary to ensure optimal performance of these applications.

• Navigation and Control: UAM navigation and control involve tasks ensuring the smooth maneuvering of the eVTOL within the urban airspace. This includes trajectory planning, obstacle avoidance with static and mobile objects, compli- ance with the airspace operation management system, battery monitoring, guidance, and control decisions, especially for autonomous UAM aircraft. The deployment of MEMS IoT- sensors, coupled with IoT-data analytics, can significantly contribute to the monitoring and regulation of UAM-based transportation. Such sensors, which include an altimeter, tachometer, inertial sensors, the Attitude and Heading Ref- erence System (AHRS), aeroelastic fluttering sensors, and shock sensors, can provide highly accurate and low-noise data, thereby improving the efficacy of vehicle tracking systems, traffic management, and inventory management. Collected data from onboard IoT sensors and information exchanged within the UAM-IoT infrastructure is essential to making real-time and rapid in-situ decisions to accommodate changes in laterals, altitude, and time variations encountered in pre-scheduled and emergency situations, e.g., an urgent landing, as well as establish tactical deconfliction strategies [7].

IV. UAM CHALLENGES ANDIOT SOLUTIONS

In this section, we investigate the main UAM challenges raised in the literature [1] and discuss how IoT can be used to alleviate them.

A. Safety and Regulatory Environment

Efficient navigation, guidance, and control of UAM aircraft are essential to ensuring the safety of UAM users, operators, and the public in general. The integration of the IoT has the potential to provide immense benefits for monitoring the air- worthiness of the aircraft, providing access to designated flight trajectories, and maintaining the UAM infrastructure. IoT- based sensors can be effectively utilized to provide predictions and alarms regarding loss of control, loss of data exchange, and system failure. Particular emphasis must be placed on smart sensors for monitoring the health of the eVTOLs, ongo- ing flight parameters, fault detection and prediction, accurate localization for take-off and landing at the right speed and heading, and reconfiguration during emergencies. IoT can also help simplify UAM port procedures, such as takeoff and landing, which are subject to non-modeled dynamics and uncertainties due to urban airspace congestion, priorities pre-assigned to certain applications, scheduling, routing, and turnaround operations, including recharging, ground mainte- nance, and passenger on-boarding/deboarding [8], [9].

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B. Air Traffic Management

The IoT ecosystem provides real-time support in moni- toring, analyzing, and controlling air-traffic flow, inclusive of managing routing decisions, resource sharing, congestion and collision prediction, and priority handling. Upgrading the UAM airspace to a remotely and/or autonomously controlled environment and organizing the interaction of the same with commercial and/or general aviation are important attributes of air-traffic management. The UAM-IoT ecosystem provides real-time support for monitoring, analyzing, and controlling air-traffic flow, such as managing routing decisions, resource sharing, predicting congestion and collisions, and prioritizing traffic. To effectively upgrade the UAM airspace to a remotely and/or autonomously controlled environment, it is necessary to observe multiple take-offs, landings, and cruise motions simultaneously within a defined area and make routing deci- sions based on factors such as frequent destinations and power requirements.

C. Noise and Community Response

IoT subsystem can be successfully utilized to supervise flight parameters and evaluate the level of generated noise.

The IoT network can also facilitate interaction with the users to assess community response to their flight experience and a general sense of acceptance of UAM as an affordable and reliable mode of transport. Other areas that can leverage the advantages offered by the IoT comprise the identification, mit- igation, and elicitation of responses to contingencies involving degraded navigation.

D. Environmental Impact

The utilization of IoT-based UAM can have a positive effect on predicting local weather statistics and analyzing long-term environmental data [10]. To make use of these benefits, the deployment and tuning of environmental monitoring sensors, the generation of dynamic weather maps, and predictive plan- ning with the help of IoT data analytics are some strategies that can be implemented in real life. Additionally, utilizing renewable energy resources can give a further advantage to supervisory applications over an extended period. For these positive environmental impacts to be achieved, low latency and high throughput are necessary, which cannot be accom- plished through conventional methods and thus require novel communications protocols.

E. Infrastructure

A functional UAM network must be able to accommodate reliable information exchange in addition to hosting take-off and landing stations, charging facilities, and control stations. It is imperative to determine where to deploy charging stations, base stations, and other IoT equipment for real-time main- tenance, location tracking, and the collection of temperature and humidity data. The proposed UAM-IoT integrated data network is able to serve the different types of links mentioned in Section III.C.2. In fact, IoT networks are well prepared to be part of a heterogeneous network ecosystem. IoT to ground

cellular base stations are supported by NB-IoT and LTE-M.

IoT connectivity to low- and high-altitude flying platforms is ensured using the well-studied Space-Air-Ground Integrated Networks (SAGIN). The inter-eVTOL can be performed via the conventional IoT mesh networks structure.

F. Security and Data Privacy

The integration of UAM operations requires the implemen- tation of comprehensive security and data privacy measures to prevent malicious agents from gaining unauthorized control of the aircraft and protect end-user privacy. To this end, con- nected eVTOL aircraft must be adequately defended against potential cybersecurity attacks and eavesdropping activities.

These swarms of IoT devices will soon occupy our urban airspace and are highly susceptible to such malicious activities, necessitating the implementation of robust security protocols and measures. Hardware-configured IoT devices that utilize end-to-end data encryption schemes can effectively mitigate the risk of cyberattacks and data leakage. Moreover, the implementation of standardized security protocols, two-factor authentication, and regular firmware updates is essential for the provision of adequate security and privacy in the rapidly expanding UAM ecosystem. Additionally, advanced IoT tech- nologies have been developed to support the deployment of a cost-effective managed Public Key Infrastructure (PKI) service for secure device authentication.

V. UAM-IOT APPLICATIONS

A. UAM-IoT Applications Categories

The UAM-IoT can offer a wide range of real-time data, available to end-users in the form of services. These services can be divided into the following categories:

• eVTOL-as-a-Service: IoT can enable UAM service providers to offer civil drone services for passenger travel.

A range of options, such as ride-sharing, personal travel, and medical transportation, may be used by end-users. These air taxis can be booked on demand depending upon the requesters’ locations, the number of passengers, and the nearby eVTOLs. To make this service possible, the UAM-IoT relies on real-time information collected by smart sensors such as GNSS and radar, wireless communication links between the service providers and end-users, and an application-specific user interface.

• Infrastructure-as-a-Service: Information related to the infrastructural and flight logistics, such as the location of hardware components associated with a UAM aircraft, the maintenance and repair service provider, the third-party owner, the availability of the vertiports, the charging stations, and pos- sible payloads that can be mounted on the UAM, is necessary for navigation and task planning. These infrastructures are not required to be owned by the UAM service providers and can be used as-a-service for smooth operation.

• Application-as-a-Service: The UAM must be reliably and securely connected to software services and application frameworks associated with the infrastructure. End-users may access the specific application interfaces depending on their requirements. For instance, an agricultural company may use

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Fig. 6:IoT-supported applications of UAM benefiting the end-users.

this service to survey the health of crops using vision-based sensors, whereas local meteorological divisions may avail themselves of weather-related information to predict changes in wind speed, rainfall, snowfall, cyclones, and air and noise pollution levels. The aircraft control stations can also avail themselves of this service for server management, maintenance and updating of databases, cybersecurity, payload balancing, power management, upgrading data routing QoS, network reconfiguration, task distribution, and decision management.

B. UAM-IoT Application Use-Cases

By leveraging the capabilities of the IoT, UAM services can be further enhanced, thereby allowing for the successful integration of UAM into a smart-living community. Primarily, UAM-IoT is envisioned to offer mobility services related to package delivery, transportation of goods, ride-sharing, personal commutes, and medical transportation. Moreover, the combination of ride-share road networks with UAM transportation can greatly reduce commute times for urban workers while providing essential services such as multi- modal transportation, disaster relief, rescue, and evacuation efficiently. Additionally, the concept of multi-modal public transit is gaining importance, as it enables the integration and coordination of UAM operations with other transportation modes, such as roads, buses, and trains. This is evidenced by the joint review of multi-modal air transportation by Volocopter and the NEOM mega-city of the Kingdom of Saudi Arabia, as well as by Archer’s plans to make UAM services available to the frequently flooded areas of southern Florida by the end of 2024.

Apart from passenger travel and parcel delivery, UAM has immense potential to provide data services to end-users.

Multiple smart sensors (thermocouple, resistance temperature detector (RTD), hygrometer, barometer, camera, lidar, etc.) may be attached to a UAM aircraft that can perform in- flight data acquisition and provide time-sensitive information.

IoT-based UAM services may also help monitor forest fires, poaching, wildlife censuses, and border patrol. Inspection of crops, soil conditions, pest control, livestock identification and count, health and habit patterns of animal husbandry, inspecting faults in manufacturing units, large machinery, and

distributed industries such as gas pipelines are other aspects of UAM applications. Surveillance and low altitude defense operations such as ground targeting combat, training, anti- collision, and noise-free military flights in cityscapes will be significantly benefited from UAM. End-users may utilize the information gathered from UAM-IoT sensors to generate fast and efficient solutions to detected problems and make better management decisions to improve productivity and garner higher profit. A non-exhaustive list of user-oriented applications has been shown in Fig. 6. Furthermore, UAM- IoT offers artificial intelligence and machine learning-based data analytics that may help reduce the operational costs of these applications and optimize multiple factors such as QoS, safety, connectivity, and efficiency.

VI. OPENRESEARCHDIRECTIONS

Presently, UAM networks are being setup around the globe, with consistent upgrades to the previously discussed IoT paradigms. However, there is still room for further improve- ments. Some of the open directions of research and develop- ment for UAM-IoT services include the following sectors:

Compatibility with 5G/6G Technology

With the recent deployment delay of 5G base stations close to US airports due to possible interference with airplane altimeters, UAM operations needs to mitigate interference with the radio altimeter, which is a safety-critical device using frequencies close to the C-band. Thereby, the accuracy of the altimeters needs to be enhanced to improve filtering and pre- vent signal leakage despite 5G deployment near the vertiports.

From a physical layer perspective, multiple efforts need to adapt the current trends to the UAM context, including non- orthogonal multiple access (NOMA), rate splitting multiple access (RSMA), and re-configurable intelligent surfaces (RIS).

Social IoT

An emerging era of social IoT (SIoT) aims to convert net- worked smart objects into autonomously communicating social objects. Incorporating SIoT networks in the UAM ecosystem can offer significant advantages to interactions between IoT devices, UAM service providers, and end-users by reducing time and computational overhead in resource and task sharing.

In a way similar to vehicular social networks (VSN), UAM social networks can be developed to merge human social networks composed of passengers, pilots, workers, stakehold- ers, and the internet-of-eVTOLs, which involves all connected vehicles and devices in the UAM ecosystem. Such UAM social networks can promote the IoT service discovery process and leverage many UAM-IoT services.

Real-time Decision Making

The data pool collected by the on-board sensors needs to be processed and analyzed in real-time so that corresponding decisions can be taken without tangible latency and with acceptable accuracy. Speed and accuracy are challenging to achieve simultaneously but are nonetheless essential for rapid

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rerouting, fail-safe control, congestion avoidance, and particu- larly for emergency takeoff and landing. Therefore, novel algo- rithms and approaches, especially the artificial intelligence and machine learning based ones, tackling the different UAM-IoT functions discussed earlier need to be ameliorated to enable an accurate, resilient, sustainable, and more autonomous UAM ecosystem. Digital twin technology can be advantageous for UAM routing and congestion avoidance simulations. A digital twin is a virtual, real-time copy of an eVTOL and the urban environments in which the eVTOLs operate. For efficient nav- igation and control, the synchronization of multiple networked flying and ground IoT devices can be performed on the digital twin instead of the physical devices.

Data Security

Security breaches often target communication interfaces, sensory devices, and supervisory control systems distributed over the UAM network, particularly due to a lack of firmware maintenance and the inadequacy of authentication protocols over time. To mitigate these risks, IoT resources can be segmented across cloud, fog, and edge servers. Edge com- puting can also offer fast processing, but limited resources and proximity to end-user devices make it vulnerable to receiving a large volume of privacy-sensitive data that needs to be protected against potential hackers. The strength of encryption of user data and flight-control commands must be regularly evaluated against new malware attacks. Furthermore, the development of autonomous control systems that can handle the dynamic environment of UAM and the integration Artificial Intelligence (AI) for improved safety and security are important research directions.

VII. CONCLUSION

In this paper, we discussed the different aspects of the UAM ecosystem, the IoT architectural framework, which can reinforce UAM paradigms and functions, the IoT-supported UAM connectivity, and the challenges of the UAM system that can be addressed by integrating IoT. We also explored the UAM applications enabled by IoT and the directions for further research related to the UAM-IoT association. We conclude that before the commercial deployment of UAM services, necessary infrastructure to leverage the benefits of IoT in UAM applications, control systems, and connectiv- ity need to be developed. Additionally, public and private partnerships between industries, local governments, regulators, operators, and infrastructure stakeholders must be encouraged.

Finally, the successful integration of UAM into a smart- living community requires engaging the public and gaining the majority of target users’ social consent for the usage of UAM in daily urban transportation.

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Available: https://arxiv.org/abs/2110.08830